CN102488514B - Method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities - Google Patents

Abstract

A method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities comprises the steps of performing system setup, and using a LabVIEW 8.6 to generate synchronizing pulse signals; respectively collecting electroencephalograph signals and electromyography signals including electroencephalograph signals and electromyography signals in autonomous movement modalities and in stimulation movement modalities; analyzing electroencephalograph and electromyography time-domain signals in the autonomous movement and stimulation movement modalities according to time domain pictures of electroencephalograph and electromyography signals of a subject in the autonomous movement and stimulation movement modalities; removing noise of the electromyography signals in the stimulation modality; performing time-frequency analysis on electroencephalograph signals based on Morlet wavelet transformation; and performing coherence analysis. The method can obtain activating or restraining information of electroencephalograph in different time frequency in initiative and passive states to be used for guiding and feeding back recovery indexes of physical disability patients of apoplexy patients and the like, thereby enabling recovery to be a quantitative process instead of a qualitative definition.

Description

Based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode

Technical field

The present invention relates to a kind of analytical method of brain myoelectricity dependency.Particularly relate to a kind of can make people understand the cerebral nerve activity how to control muscular movement based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode.

Background technology

At present, some international bodies and scholar begin to pay close attention to and be devoted to the research of the mutual relation of brain-myoelectricity, as U.S. Univ State Cleveland and Cleveland hospital to the research of the EEG-EMG dependency under the single task, disclosed and subtracted gradually reduction by the Functional Surface muscle coupling performance due to the muscle fatigue; Myoelectricity coherence when New South Wales,Australia university moves simultaneously to brain-myoelectricity dependency and both hands between two handss studies, and inquires into the relation of signal source and frequency from the frequency range difference; Roma Univ. is by inquiring into the judgement balance to the research of elite athlete, ordinary movement person and non athlete's brain-myoelectricity dependency; The spinal function variation with advancing age of human body is inquired into to all ages and classes stage people's brain-myoelectricity correlation research by the London University; Freiburg, Germany university is mainly studied is the myoelectricity that produces under the different grip size and the coherence of brain electricity.In addition, also have some universities and research institution also the brain under the different condition-myoelectricity dependency to be studied abroad, domestic still very limited to research in this respect.

Up to now, most researchs are inquired into the autonomous action aspect that all concentrates on the experimenter to the relation of brain-myoelectricity.But for disability patient's athletic rehabilitation, except autonomous action, the brain electricity, the myoelectricity rule that stimulate action mode to cause are noticeable equally, and the electricity irritation of domestic and international application neuromuscular improves disability patient moving function and obtained good curative effect.Following content shows this point:

Germany Rutgers university studies show that, after patient's femoral nerve injury, uses low-frequency electrostimulating can accelerate functional rehabilitation; The Hong Kong Polytechnic University studies show that, when functional electric stimulation is applied to Healthy People and makes it produce hand exercise, can cause the activation of brain corresponding sports district and sensory region with it; The people's such as Liu Huihua research has also proved this point by the discussion to somatosensory evoked potential and Motion Evoked Potential; After Kimberley etc. act on the patients with cerebral apoplexy limbs with functional electric stimulation, find that the cerebral cortex signal of telecommunication obviously increases, the limbs of patient function also has clear improvement.Correlational study shows, the neuromuscular electricity irritation can help the patient to finish joint motion, and correct joint motions sensation and muscle contraction are felt to pass to brain, promotes the restructuring of brain function and activate idle nervous pathway to substitute function of nervous system in damaged condition.

Summary of the invention

Technical problem to be solved by this invention is, provide a kind of and can access initiatively and activation or the inhibition information of passive hypencephalon electricity different frequency range, instruct thus and feed back the patients with limb disabilities such as apoplexy the rehabilitation index based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode.

The technical solution adopted in the present invention is: a kind of based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, include following steps:

1) carries out system's setting, that is, use LabVIEW8.6 to produce synchronization pulse;

2) carry out respectively eeg signal acquisition and electromyographic signal collection, comprising: independently move EEG signals and electromyographic signal under the mode, stimulate EEG signals and electromyographic signal under the action mode;

3) according to the experimenter under the autonomous action mode and stimulate brain electromyographic signal time-domain diagram under the action mode independently to move under the mode and stimulate brain myoelectricity time-domain signal analysis under the action mode;

4) to stimulating the electromyographic signal under the mode to carry out denoising;

5) carry out time frequency analysis based on the EEG signals of wavelet transformation, described time frequency analysis is the wavelet transformation that adopts based on Morlet;

6) carry out coherent analysis

Coherence factor is a parameter of gauge signal dependency, and he is defined as:

${\mathrm{<figure-callout\; id="1"\; label="Coh\; c"\; filenames="HDA0000117956250000062.png,HDA0000117956250000071.png"\; state="\{\{state\}\}">Coh</figure-callout>}}_c\left(f\right)=\frac{{\left|{\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000062.png,HDA0000117956250000071.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)\right|}^2}{\left|{\mathrm{SP}}_{c1}\left(f\right)\right|\&times;\left|{\mathrm{SP}}_{c2}\left(f\right)\right|}$

Wherein, S _{C1, c2}(f) expression c1 leads the signal that connects the place and c2 and leads the signal that connects the place at the cross-spectrum at given frequency f place, the cross-spectrum of EEG signals and electromyographic signal specifically among the present invention, and it is defined as:

${\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000062.png,HDA0000117956250000071.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C1}_i\left(f\right){\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="HDA0000117956250000062.png"\; state="\{\{state\}\}">C</figure-callout>}2}_2^{*}\left(f\right)$

The coherence factor value is worth larger dependency better between 0 to 1.

The described use of step 1 LabVIEW8.6 produces synchronization pulse and comprises following process:

(1) sample rate and sampling configuration are set;

(2) generate the monocycle analog waveform;

(3) begin output;

(4) judge: whether the remainder of acquisition time/10 is to continue after the light-off to judge again, otherwise continue after the bright light to judge again greater than 2.

The described autonomous action mode of step 2 is specifically: the experimenter had a rest for 10 seconds; Open LabVIEW, the generation cycle is that 10s, dutycycle are 0.2 square-wave pulse, so that triggering at high level, display lamp is lit, and lasting 2s, display lamp is closed 8s then, and when display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Record simultaneously 60 seconds of C3 among the brain electric conductance connection figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal, square-wave pulse signal.

The described stimulation of step 2 is moved mode specifically: the experimenter had a rest for 10 seconds, close LabVIEW, open electric pulse stimulator, and boost pulse is adjusted to stimulus frequency 0.1Hz, width 100 μ S, initial current 5mA, stimulation location is experimenter's right arm median nerve, and stimulation point is the right upper extremity far-end, near canalis carpi section, and adjust stimulator current intensity according to experimenter's middle finger action degree, to experimenter's middle finger obvious flexing action can be arranged under stimulating till; Record simultaneously 60 seconds of C3 among the stimulation state hypencephalon electric conductance connection figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal.

Step 4 is described to be to adopt 2 crest threshold detection algorithm to stimulating electromyographic signal under the mode to carry out denoising: at first, and in the original electromyographic signal input computer under the stimulation action mode that gathers; Computer begins reading out data, program is calculated the absolute value of the electromyographic signal in this segment data and is got maximum, this maximum correspondence the amplitude that stimulates peak value, subsequently programming two initial threshold: one is high level (HT), for the maximum crest value that detects divided by 2; One is low level (LT), is 1/20 of maximum crest value;

Computer when running into first low level the time, records this point in the process that detects; Continue scan-data, when running into high level, continue scanning, when running into second high level and second low level, record this segment data, and the data between two low levels all are set to 0;

Perhaps in the process that computer is detecting, when running into first low level, continue scanning, next data value does not surpass high level, but has run into second low level, and the signal that obtain this moment is useful electromyographic signal, skips this segment data, continues scanning.

Of the present invention based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, under autonomous action mode, when action produces, low-frequency component in EEG signals, be that α frequency range (8-13Hz) has produced that event is relevant to desynchronize, i.e. relevant (ERD) phenomenon that desynchronizes of event; Then, about a second so that offside brain electricity has produced relevant phenomenon, i.e. event related synchronization (ERS) phenomenon of desynchronizing of event in β frequency range (14-30).And stimulating under the action mode, because the impact of the electric pulse that exists so that the low frequency signal of brain electricity is always comparatively active, but when the action beginning, still has the existence of ERD phenomenon always, then there is the energy liter of β frequency range in the second.The present invention obtain initiatively and passive hypencephalon electricity different frequency range activation or inhibition information, be used for thus instructing and feeding back the rehabilitation index of the patients with limb disabilities such as apoplexy, so that rehabilitation no longer is a qualitatively concept, more moved towards a quantitative process.

Description of drawings

Fig. 1 is based on independently, stimulates the brain myoelectricity correlation analysis block diagram of action;

Fig. 2 is Labview 8.6 lock-out pulse FB(flow block)s;

Fig. 3 is brain electric conductance connection schematic diagram;

Fig. 4 (a) is the brain electromyographic signal time-domain diagram of experimenter under autonomous action mode;

Fig. 4 (b) is that the experimenter is at the brain electromyographic signal time-domain diagram that stimulates under the action mode;

Fig. 5 is based on the program control flow chart of 2 crest threshold detection algorithm;

Fig. 6 is based on the electromyographic signal comparison diagram under the stimulation mode of 2 crest threshold test;

Fig. 7 (a) is the frequency spectrum design sketch of the EEG signals of experimenter under autonomous action mode;

Fig. 7 (b) is that the experimenter is at the frequency spectrum design sketch that stimulates the EEG signals under the action mode;

Fig. 8 (a) is the brain myoelectricity coherence result of experimenter under autonomous action mode design sketch;

Fig. 8 (b) is that the experimenter is at the design sketch that stimulates the brain myoelectricity coherence result under the action mode;

Fig. 9 is coherence the count relation of experimenter under two kinds of actions of different-waveband mode,

Wherein: the left side is independently to move under the mode, and the right is to stimulate under the action mode.

Among the figure:

1: cerebral cortex 2: muscle of upper extremity

3: surface electrode 4: surface electrode

5: independently move 6: stimulate action

7: eeg amplifier 8: myoelectricity amplifier

9: the biological electricity of digitized gathers 10: date processing

The specific embodiment

Below in conjunction with embodiment and accompanying drawing to of the present invention based on autonomous, stimulate the analytical method of the brain myoelectricity dependency under the action mode to make a detailed description.

Of the present invention based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, include following steps:

1) carries out system's setting, that is, use LabVIEW8.6 to produce synchronization pulse;

Eeg amplifier shown in Figure 1 and myoelectricity amplifier are to be integrated on the same instrument---the digital brain myoelectricity of the four-way analyser (Micromed Brain Quick EEG) that Micromed company produces, and what surface electrode was chosen is the Ag-AgCl electrode; In whole experimentation, experimenter's boost pulse is produced by electric pulse stimulation instrument (being provided by the digital brain myoelectricity of four-way analyser), and this instrument can produce frequency range at the electric pulse of 0.1Hz-10Hz; Lock-out pulse is produced by virtual instrument of LabVIEW 8.6.

Described LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a kind of development environment of patterned programming language, it is accepted by industrial quarters, academia and research laboratory widely, is considered as data acquisition and the instrument control software of a standard.LabVIEW is integrated and the repertoire of the hardware that satisfies GPIB, VXI, RS-232 and RS-485 agreement and data collecting card communication.It is also built-in is convenient to use the built-in function of the software standards such as TCP/IP, ActiveX.This is a powerful and software flexibly.Utilize it can set up easily the virtual instrument of oneself, its patterned interface is so that programming and use procedure vivid and interesting all.

In the present invention, the concrete operations of using LabVIEW8.6 to produce synchronization pulse comprise following process:

(1) sample rate and sampling configuration are set;

(2) generate the monocycle analog waveform;

(3) begin output;

(4) judge: whether the remainder of acquisition time/10 is to continue after the light-off to judge again, otherwise continue after the bright light to judge again greater than 2.

2) carry out respectively eeg signal acquisition and electromyographic signal collection, comprising: independently move EEG signals and electromyographic signal under the mode, stimulate EEG signals and electromyographic signal under the action mode;

The collection of the EEG signals 10-20 electrode of adopting international standards is placed standard, by electrode cap electrode is linked to each other with scalp.Because the moving region of brain control human body is apparent in view in C3, C4 zone, so the EEG signal gathers at C3, C4 place.Adopt the single-stage method of leading, brain electricity reference electrode A1, A2 lead and are connected respectively to left and right sides ear-lobe and use as indifferent electrode, as shown in Figure 3.

Electromyographic signal collection is under different action patterns, require the active of experimenter's middle finger or passive action (passive the counting on one's fingers after namely being upset), the target muscle that needs transfer and participate in is flexor digitorum superficialis (flexor digitorum superficialis, FDS).

Eeg signal acquisition detailed process under the described autonomous action mode is: the experimenter had a rest for 10 seconds; Open LabVIEW, the generation cycle is that 10s, dutycycle are 0.2 square-wave pulse, so that triggering at high level, display lamp is lit, and lasting 2s, display lamp is closed 8s then, and when display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Record simultaneously among the brain electric conductance connection figure C3, C4 place EEG signals (shown in Fig. 3), flexor digitorum superficialis (FDS) and locate 60 seconds of electromyographic signal, square-wave pulse signal.

Eeg signal acquisition under the described stimulation action mode is specifically: the experimenter had a rest for 10 seconds, close LabVIEW, open electric pulse stimulator, and boost pulse is adjusted to stimulus frequency 0.1Hz, width 100 μ S, initial current 5mA, stimulation location is experimenter's right arm median nerve, and stimulation point is the right upper extremity far-end, near canalis carpi section, and adjust stimulator current intensity according to experimenter's middle finger action degree, to experimenter's middle finger obvious flexing action can be arranged under stimulating till; Record simultaneously among the stimulation state hypencephalon electric conductance connection figure C3, C4 place EEG signals, flexor digitorum superficialis (FDS) and locate 60 seconds of electromyographic signal.

3) according to the experimenter under the autonomous action mode and stimulate brain electromyographic signal time-domain diagram under the action mode independently to move under the mode and stimulate brain myoelectricity time-domain signal analysis under the action mode;

What Fig. 4 provided is that the experimenter is at the brain myoelectricity time-domain signal under the autonomous action mode, under the stimulation action mode, in Fig. 4 (a), can see that electromyographic signal is in whole course of action, amplitude has obvious variation, explanation is when muscle contraction or diastole, and the muscle electrical activity will enliven during than tranquillization.In Fig. 4 (b), expression be the experimenter at the brain electromyographic signal time-domain diagram that stimulates under the action mode.In this figure, can find out under the stimulation of electric pulse, experimenter's electromyographic signal amplitude is higher, why produces this phenomenon, is because electric pulse has polluted electromyographic signal, therefore need to carry out denoising to stimulating the electromyographic signal under the mode.

4) to stimulating the electromyographic signal under the mode to carry out denoising;

Stimulate under the action mode, because have the interference of stimulus signal, especially stimulation to occur simultaneously with inducing myoelectric potential on muscle, and stimulating electrode is close with the recording electrode position, and gathering pure electromyographic signal just has certain difficulty.Generally speaking, the more common electromyographic signal of the amplitude of stimulus signal is higher, and the output of powerful stimulus signal can be infected comparatively responsive myoelectricity acquisition system, causes the stimulation interference problem.Therefore, the present invention has adopted " 2 crest threshold detection algorithm " to weaken the interference of boost pulse.

Set high level and the low level of crest among the present invention according to the absolute value of electromyographic signal data, thereby detected positive and negative stimulation crest, and then the filtering stimulus signal has stayed complete electromyographic signal simultaneously.Because boost pulse large spike waveform normally, its amplitude and time constant are subjected to some factor co-controllings, comprise the factors such as the setting that stimulates output current, amplifier, electrode position, stimulus modelity.If the position of electromyographic signal collection electrode enough away from stimulating electrode, stimulates interference and inducing myoelectric potential signal can not produce aliasing so.

Described to stimulating the electromyographic signal under the mode to carry out denoising as shown in Figure 5: at first, in the original electromyographic signal input computer under the stimulation action mode that gathers; Computer begins reading out data, program is calculated the absolute value of the electromyographic signal in this segment data and is got maximum, this maximum correspondence the amplitude that stimulates peak value, subsequently programming two initial threshold: one is high level (HT), for the maximum crest value that detects divided by 2; One is low level (LT), is 1/20 of maximum crest value;

Computer when running into first low level the time, records this point in the process that detects; Continue scan-data, when running into high level, continue scanning, when running into second high level and second low level, record this segment data, and the data between two low levels all are set to 0;

Perhaps in the process that computer is detecting, when running into first low level, continue scanning, next data value does not surpass high level, but has run into second low level, and the signal that obtain this moment is useful electromyographic signal, skips this segment data, continues scanning.

In this program, whenever detect the just thereupon filtering of a crest, therefore can filtering stimulate interference waveform.

At last, through the electromyographic signal denoising, obtain the electromyographic signal under the comparatively pure stimulation mode shown in Fig. 6.

5) carry out time frequency analysis based on the EEG signals of wavelet transformation, described time frequency analysis is the wavelet transformation that adopts based on Morlet;

Described Morlet wavelet transformation is a kind of of continuous wavelet transform, and basic thought is: continuous time signal s (t) and Morlet small echo w (t, f) are carried out convolution, thereby obtain time dependent time-frequency Energy distribution, namely

TF(t，f)＝|w(t，f) ^*s(t)| ²

Morlet small echo w (t, f) is a kind of Gaussian function that copies modulation, in time domain (standard deviation _t) and frequency domain (standard deviation _f) on all have Gauss distribution, for certain frequency f, its expression formula is:

$w(t,f)=\mathrm{Aexp}(-{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HDA0000117956250000062.png"\; state="\{\{state\}\}">t</figure-callout>}}^2/2{\mathrm{\&sigma;}}_t^2)\exp \left(2\mathrm{i\&pi;ft}\right)$

Wherein, σ _t=1/2 π σ _f, $A={\left({\mathrm{\&sigma;}}_t{\mathrm{\&pi;}}^{\frac{1}{2}}\right)}^{-\frac{1}{2}}$

A is normalization factor, its objective is that the energy that will guarantee wavelet basis itself is 1.

Morlet small echo family has constant ratio f/ σ _f(general value is greater than-5 in actual applications), so the corresponding σ of different frequency f _fAnd σ _tBe different, namely it has variable time frequency resolution at whole time-frequency plane: can provide high temporal resolution at high frequency region, can provide high frequency discrimination at low frequency range.

As shown in Figure 7, provided the experimenter at spectrogram autonomous, that stimulate the EEG signals under the action mode.

From Fig. 7 (a), Fig. 7 (b) two width of cloth figure, can clear and definite finding out, under autonomous action mode, when action produced, at the low-frequency component of EEG signals, namely α frequency range (8-13Hz) had produced that event is relevant to desynchronize, i.e. the ERD phenomenon; Then, about a second so that offside brain electricity has produced event related synchronization phenomenon, i.e. ERS phenomenon in β frequency range (14-30).And stimulating under the action mode, because the impact of the electric pulse that exists so that the low frequency signal of brain electricity is always comparatively active, but when the action beginning, still has the existence of ERD phenomenon always, then exist the energy of β frequency range to raise in the second.

6) carry out coherent analysis

Coherence factor is a parameter of gauge signal dependency, and he is defined as:

${\mathrm{<figure-callout\; id="1"\; label="Coh\; c"\; filenames="HDA0000117956250000062.png,HDA0000117956250000071.png"\; state="\{\{state\}\}">Coh</figure-callout>}}_c\left(f\right)=\frac{{\left|{\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000062.png,HDA0000117956250000071.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)\right|}^2}{\left|{\mathrm{SP}}_{c1}\left(f\right)\right|\&times;\left|{\mathrm{SP}}_{c2}\left(f\right)\right|}$

Wherein, S _{C1, c2}(f) expression c1 leads the signal that connects the place and c2 and leads the signal that connects the place at the cross-spectrum at given frequency f place, the cross-spectrum of EEG signals and electromyographic signal specifically in the present invention, and it is defined as:

${\mathrm{<figure-callout\; id="1"\; label="S\; c"\; filenames="HDA0000117956250000062.png,HDA0000117956250000071.png"\; state="\{\{state\}\}">S</figure-callout>}}_c\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C1}_i\left(f\right){\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="HDA0000117956250000062.png"\; state="\{\{state\}\}">C</figure-callout>}2}_2^{*}\left(f\right)$

The coherence factor value is worth larger dependency better between 0 to 1.

Among Fig. 8, Fig. 8 (a) is the brain myoelectricity coherence result of experimenter under autonomous action mode, and Fig. 8 (b) is that the experimenter is the brain myoelectricity coherence result who stimulates under the action mode.

Fig. 9 has provided the coherence's relation of counting under two kinds of actions of different-waveband mode.

Claims (5)

1. one kind based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, it is characterized in that, includes following steps:

1) carries out system's setting, that is, use LabVIEW8.6 to produce synchronization pulse;

2) carry out respectively eeg signal acquisition and electromyographic signal collection, comprising: independently move EEG signals and electromyographic signal under the mode, stimulate EEG signals and electromyographic signal under the action mode; Under the autonomous action mode, tested need are finished right hand middle finger flexing action initiatively; Stimulate under the action mode, utilize electrostimulator to stimulate the median nerve of tested right arm, its right hand middle finger flexing of finishing under stimulating is moved.Under these two mode, the brain electricity all gathers the brain electricity that C3 leads and locates, and myoelectricity all gathers the myoelectricity at right arm flexor digitorum superficialis place;

3) according to the experimenter under the autonomous action mode and stimulate brain electromyographic signal time-domain diagram under the action mode independently to move under the mode and stimulate brain myoelectricity time-domain signal analysis under the action mode;

4) utilize 2 crest threshold detection algorithm to stimulating the electromyographic signal under the mode to carry out denoising;

5) carry out time frequency analysis based on the EEG signals of wavelet transformation, described time frequency analysis is the wavelet transformation that adopts based on Morlet;

6) carry out coherent analysis

Coherence factor is a parameter of gauge signal dependency, and he is defined as:

${\mathrm{Coh}}_{c1,c2}\left(f\right)=\frac{{\left|S_{c1,c2}\left(f\right)\right|}^2}{\left|{\mathrm{SP}}_{c1}\left(f\right)\right|\&times;\left|{\mathrm{SP}}_{c2}\left(f\right)\right|}$

Wherein, S _{C1, c2}(f) expression c1 leads the signal that connects the place and c2 and leads the signal that connects the place at the cross-spectrum at given frequency f place, the cross-spectrum of EEG signals and electromyographic signal specifically among the present invention, and it is defined as:

$S_{c1,c2}\left(f\right)=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C1}_i\left(f\right)C{2}_2^{*}\left(f\right)$

The coherence factor value is worth larger dependency better between 0 to 1.

2. according to claim 1ly it is characterized in that based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, described uses of step 1 LabVIEW8.6 generation synchronization pulse comprises following process:

(1) sample rate and sampling configuration are set;

(2) generate the monocycle analog waveform;

(3) begin output;

(4) judge: whether the remainder of acquisition time/10 is to continue after the light-off to judge again, otherwise continue after the bright light to judge again greater than 2.

3. according to claim 1ly it is characterized in that based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, the described autonomous action mode of step 2 specifically: the experimenter had a rest for 10 seconds; Open LabVIEW, the generation cycle is that 10s, dutycycle are 0.2 square-wave pulse, so that triggering at high level, display lamp is lit, and lasting 2s, display lamp is closed 8s then, and when display lamp was bright, the experimenter did the action of right hand middle finger flexing fast; Record simultaneously 60 seconds of C3 among the brain electric conductance connection figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal, square-wave pulse signal.

4. according to claim 1 based on independently, stimulate the analytical method of the brain myoelectricity dependency under the action mode, it is characterized in that, the described stimulation of step 2 is moved mode specifically: the experimenter had a rest for 10 seconds, close LabVIEW, open electric pulse stimulator, and boost pulse is adjusted to stimulus frequency 0.1Hz, width 100 μ S, initial current 5mA, stimulation location is experimenter's right arm median nerve, stimulation point is the right upper extremity far-end, near canalis carpi section, and adjust stimulator current intensity according to experimenter's middle finger action degree, to experimenter's middle finger obvious flexing action can be arranged under stimulating till; Record simultaneously 60 seconds of C3 among the stimulation state hypencephalon electric conductance connection figure, C4 place EEG signals, flexor digitorum superficialis place electromyographic signal.

5. according to claim 1 based on analytical method autonomous, that stimulate the brain myoelectricity dependency under the action mode, it is characterized in that, step 4 is described to be to adopt 2 crest threshold detection algorithm to stimulating electromyographic signal under the mode to carry out denoising: at first, and in the original electromyographic signal input computer under the stimulation action mode that gathers; Computer begins reading out data, program is calculated the absolute value of the electromyographic signal in this segment data and is got maximum, this maximum correspondence the amplitude that stimulates peak value, subsequently programming two initial threshold: one is high level (HT), for the maximum crest value that detects divided by 2; One is low level (LT), is 1/20 of maximum crest value;

Computer when running into first low level the time, records this point in the process that detects; Continue scan-data, when running into high level, continue scanning, when running into second high level and second low level, record this segment data, and the data between two low levels all are set to 0;

Perhaps in the process that computer is detecting, when running into first low level, continue scanning, next data value does not surpass high level, but has run into second low level, and the signal that obtain this moment is useful electromyographic signal, skips this segment data, continues scanning.

Images